Visual Token Embedding. We split the input video sequence into non-overlapping patches with size \(s_T\times s_H\times s_W\)  and adopt a linear mapping function to project each flattened patch into \(D\) -dimension vector space. Following MViT [1]}, this is equivalent to a convolutional layer with a stride of \(s_T\times s_H\times s_W\)  and a number of output channels of \(D\) . This operation results into \(N\)  tokens where \(N=\frac{T}{s_T}\times \frac{H}{s_H}\times \frac{W}{s_W}\) . In addition, the learnable positional embedding \({E}\in \mathbb {R}^{N\times D}\)  is added to the local tokens. Our key insight is to further embed global information into a global visual token using convolutional operations, as illustrated in Fig. REF (a). Since there is a single global token, it does not require positional embedding.
In our experiments, we
examine multiple strategies to embed the global visual token.
